When I tell founders that serious software can be built without a full-time engineering team, the reaction splits into two camps. Half of them are relieved — they have been staring at engineering salary benchmarks with mild terror. The other half are skeptical in a way that suggests they think I am about to sell them something.

I am going to try to make the case honestly, including the parts where this does not work.

The assumption worth questioning

The conventional wisdom is that if you need engineering work done seriously, you hire engineers full-time. This made more sense when the marginal cost of a developer’s hour was the main variable. That is no longer the only variable.

What changed is that AI tooling has compressed the time required for specific categories of engineering work — specifically the repetitive, well-specified work that used to occupy a significant portion of any developer’s week. Boilerplate generation, test writing, documentation, standard API integrations. These have not disappeared as tasks, but they take much less human time than they did three years ago.

When a developer using AI tools can produce the equivalent of two or three developer-days of output in one, the calculus on how many engineers you need changes.

What the alternative model actually looks like

The setup I see working consistently for early-stage founders has two components.

First: a technical decision-maker — either a fractional CTO or a strong technical co-founder — who owns architecture, vendor decisions, code review standards, and the strategic technology choices. This person costs $5,000–$15,000/month for meaningful engagement, less than a senior in-house engineer in most markets, and they bring perspective that a junior engineer cannot provide.

Second: an AI-native development pod for execution. This handles the actual building — features, integrations, tests, documentation. At $15/hr for an AI-native team, the total monthly cost for active development is typically $20,000–$40,000, including project management and quality oversight.

The combined model often costs less than one senior US engineer and produces significantly more output. I am aware that sentence sounds like marketing. The math checks out when you run it.

Where this breaks down

This is not a universal answer and I do not want to present it as one.

If your product requires deep, specialised expertise — novel algorithms, complex distributed systems, cutting-edge ML research — the flexibility of this model works against you. Institutional knowledge matters in those domains. A team that has been thinking about your specific problem for a year thinks differently about it than a team engaging with a well-defined spec. That expertise premium is real.

It also breaks down at scale. There is a stage of company — usually post-Series A, growing fast — where you need engineering culture, technical leadership in the room, and engineers who own domains long-term. At that point, the hybrid model is a bridge you have already crossed. Hiring full-time is the right move.

The question is whether you get to that stage faster by starting lean. For most of the founders I work with, the answer is yes — because burning runway on a full-time engineering salary before you have found product-market fit is one of the more reliable ways to not find product-market fit.

The honest risk

The risk of this model is picking the wrong pod. A cheap pod without a real process, without a named human lead, without visibility — that produces code quickly and produces the wrong code. The cost savings evaporate when you are paying twice to rebuild what was built wrong the first time.

The model is only as good as the execution partner. Which is advice that applies to full-time hiring too, honestly — but the stakes feel different when you are not seeing someone every day.